拟合`gls`模型后，将`joint_tests`映射到列表 [英] Map `joint_tests` to a list after fitting a `gls` model

问题描述

我正在尝试从具有以下代码的列表中获取具有 emmeans :: joint_tests()的3型ANOVA表.我不完全理解该错误信息.指导我的代码来自 http://pages.stat.wisc.edu/~yandell/R_for_data_sciences/curate/tidyverse.html

I am trying to get the type 3 ANOVA table with emmeans::joint_tests() from a list with the following code. I don't fully understand the error message. The code that tutors me came from http://pages.stat.wisc.edu/~yandell/R_for_data_sciences/curate/tidyverse.html

library(dplyr)
library(nlme)
library(emmeans)
data("diamonds")
diamonds %>%
  split(.$cut) %>%
  map(~ gls(price ~ x + y + z,  
  weights = varIdent(form = ~ 1|color),
  data = .))%>%
map(summary)

错误消息似乎表明我以某种方式保存了我的个人模型，然后应用了 joint_tests .我不明白的是，为什么工作流适用于 summary ，而不适用于 joint_tests .当我们拟合单个模型时，分别打印摘要表或ANOVA表的是 summary(model)或 joint_tests(model).

The error message seems to suggest that I save my individual models somehow and then apply joint_tests. What I don't understand is why the workflow works for summary but not for joint_tests. When we fit single models, it's summary(model) or joint_tests(model) that prints the summary table or the ANOVA table, respectively.

data("diamonds")
diamonds %>%
  split(.$cut) %>%
  map(~ gls(price ~ x + y + z,  
  weights = varIdent(form = ~ 1|color),
  data = .))%>%
map(joint_tests)

Error in (function (object, at, cov.reduce = mean, cov.keep = get_emm_option("cov.keep"), : Perhaps a 'data' or 'params' argument is needed

使用 map(〜joint_tests)给出了一个奇怪的列表

Using map(~ joint_tests) gave a weird list like this

$Fair
function (object, by = NULL, show0df = FALSE, cov.reduce = range, 
    ...) 
{
    if (!inherits(object, "emmGrid")) {
        args = .zap.args(object = object, cov.reduce = cov.reduce, 
            ..., omit = "submodel")
        object = do.call(ref_grid, args)
    }
    facs = setdiff(names(object@levels), by)
    do.test = function(these, facs, result, ...) {
        if ((k <- length(these)) > 0) {
            emm = emmeans(object, these, by = by, ...)
            tst = test(contrast(emm, interaction = "consec"), 
                joint = TRUE, status = TRUE)
            tst = cbind(ord = k, `model term` = paste(these, 
                collapse = ":"), tst)
            result = rbind(result, tst)
            last = max(match(these, facs))
        }
        else last = 0
        if (last < (n <- length(facs))) 
            for (i in last + seq_len(n - last)) result = do.test(c(these, 
                facs[i]), facs, result, ...)
        result
    }
    result = suppressMessages(do.test(character(0), facs, NULL, 
        ...))
    result = result[order(result[[1]]), -1, drop = FALSE]
    if (!show0df) 
        result = result[result$df1 > 0, , drop = FALSE]
    class(result) = c("summary_emm", "data.frame")
    attr(result, "estName") = "F.ratio"
    attr(result, "by.vars") = by
    if (any(result$note != "")) {
        msg = character(0)
        if (any(result$note %in% c(" d", " d e"))) 
            msg = .dep.msg
        if (any(result$note %in% c("   e", " d e"))) 
            msg = c(msg, .est.msg)
        attr(result, "mesg") = msg
    }
    else result$note = NULL
    result
}
<bytecode: 0x7ff68eb4a0a8>
<environment: namespace:emmeans>

$Good
function (object, by = NULL, show0df = FALSE, cov.reduce = range, 
    ...) 
{

这是我在没有列表的情况下进行 joint_tests 的方式.

Here is how I did joint_tests without a list.

diamond.gls <-  gls(price ~ x + y + z,  
  weights = varIdent(form = ~ 1|color),
  data = diamonds)
joint_tests(diamond.gls)
model term df1      df2  F.ratio p.value
 x            1 14311.72 4898.859 <.0001 
 y            1 12964.08   46.231 <.0001 
 z            1  8380.71   24.576 <.0001

请查看如何解决该问题.谢谢.

Please see how I can fix it. Thank you.

推荐答案

出于我将要调查的原因， joint_tests()是 data 参数时需要> gls 模型，至少是在函数体内调用时.为了克服这个问题，我们需要编写一个适合模型并运行 joint_tests()的函数.这是一个平行的例子:

For reasons I will investigate, joint_tests() needs the data argument when it is a gls model, at least when called within the body of a function. To overcome this, we need to write a function that fits the model and runs joint_tests(). Here is a parallel illustration:

mod_jt = function(dat) {
  mod = gls(breaks ~ tension, data = dat)
  joint_tests(mod, data = dat)
}

warpbreaks %>% split(.$wool) %>% map(mod_jt) 

...，我们得到结果:

... and we get the results:

$A
 model term df1 df2 F.ratio p.value
 tension      2  24   7.288 0.0034 


$B
 model term df1 df2 F.ratio p.value
 tension      2  24   4.059 0.0303 

我认为您拥有的代码将与 lm 模型一起使用，至少可以与 emmeans *

I think the code you had will work with an lm model, at least with the newest version of emmeans*

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